Mr. Cai Davies Profile

Cai Davies

at Cardiff Univ

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Proceedings Article | 7 June 2024 Presentation + Paper

Vector symbolic open source information discovery

Cai Davies, Sam Meek, Philip Hawkins, Benomy Tutcher, Graham Bent, Alun Preece

Proceedings Volume 13051, 1305118 (2024) https://doi.org/10.1117/12.3013447

KEYWORDS: Semantics, Binary data, Data modeling, Transformers, Electromagnetic coupling, Defense and security, Web 2.0 technologies, Matrices, Vector spaces, Surgery

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Combined, joint, intra-governmental, inter-agency and multinational (CJIIM) operations require rapid data sharing without the bottlenecks of metadata curation and alignment. Curation and alignment is particularly infeasible for external open source information (OSINF), e.g., social media, which has become increasingly valuable in understanding unfolding situations. Large language models (transformers) facilitate semantic data and metadata alignment but are inefficient in CJIIM settings characterised as denied, degraded, intermittent and low bandwidth (DDIL). Vector symbolic architectures (VSA) support semantic information processing using highly compact binary vectors, typically 1-10k bits, suitable in a DDIL setting. We demonstrate a novel integration of transformer models with VSA, combining the power of the former for semantic matching with the compactness and representational structure of the latter. The approach is illustrated via a proof-of-concept OSINF data discovery portal that allows partners in a CJIIM operation to share data sources with minimal metadata curation and low communications bandwidth. This work was carried out as a bridge between previous low technology readiness level (TRL) research and future higher-TRL technology demonstration and deployment.

Proceedings Article | 12 June 2023 Presentation + Paper

Semantic vector space mapping in edge of network multi-domain operations

Graham Bent, Douglas Summers-Stay, Alun Preece, Yuhua Li, Lewys Davies

Proceedings Volume 12538, 125380W (2023) https://doi.org/10.1117/12.2663056

KEYWORDS: Vector spaces, Semantics, Binary data, Neural networks, Associative arrays, Matrices, Sensors, Brain, Artificial intelligence, Internet of things

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Artificial Intelligence applications are increasingly making use of vector embedding techniques to achieve impressive results in many application domains. Semantic Vector Spaces (SVS’s) are constructed using semantic vector embedding techniques that learn vector representations of data across multiple domains. An important application area enabled by such techniques is the capability to represent software services and service workflows as semantic hypervectors. Previous work has shown how these hypervector representations have significant advantages over alternative schemes for decentralized service workflow construction, particularly in low communications bandwidth and energy constrained environments that are typical in multi-domain operations. SVS construction usually assumes that all the data required to construct the semantic vector space is available centrally. However, in multi-domain operations different partners may not be willing to share the training data necessary to construct a common multi-domain SVS. Hence semantic hypervectors representing similar services or workflows but constructed from different training data by different partners cannot be discovered and used. In this paper we focus on how it is possible to map semantic hypervectors between partner SVS’s, so that complementary services and workflows developed and owned by different partners can be discovered and used to achieve mission goals. The paper describes techniques for generating the required mapping that require a minimum exchange of information between the different partners; demonstrate how it is possible to do this for semantic hypervectors that use different types of encoding (eg., real valued, binary, sparse slot-encoding); and illustrates how the mapping can be implemented in various multi-domain operational settings.

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